Agent Ecosystem. Training

Fine-Tune AI Agents on Your Proprietary Data

Custom agent training that adapts models to your domain knowledge, business logic, and operational patterns for maximum accuracy in your context.

Training Pipeline
Your Data
Docs, logs, tickets
Preprocessing
Clean & chunk
Fine-tuning
GPU cluster
Evaluation
Benchmark suite
Deploy
Production model
14-day training cycle · 3× accuracy improvement · 100% data privacy
Accuracy improvement
14 days
Training cycle
100%
Data privacy
Continuous
Learning loop

Overview

What is agent training?

General-purpose AI models are trained on the internet. Your business runs on proprietary data, internal documents, support tickets, code, and domain expertise that no public model has ever seen. Agent training fine-tunes models on that private corpus so your agents answer accurately in your specific context, follow your business rules, and improve continuously as new data arrives.

What's included

Data ingestion

Connect structured and unstructured data sources, databases, document stores, APIs, and file systems, with pre-built connectors.

Preprocessing pipeline

Automated cleaning, deduplication, chunking, and formatting to prepare training data to the exact specifications each model requires.

Fine-tuning infrastructure

Managed GPU clusters spin up for each training run and tear down automatically, so you pay only for compute you actually use.

Evaluation framework

Domain-specific benchmark suites measure accuracy, hallucination rate, and task completion before any model reaches production.

A/B testing

Run the fine-tuned model alongside the base model in shadow mode to validate improvements against real production traffic before full rollout.

Continuous learning

Feedback loops automatically surface low-confidence responses for human review and incorporate corrections into the next training cycle.

How it works

From setup to production

01

Collect

Connect your data sources. We ingest documents, conversation logs, structured records, and any other domain data you have.

02

Prepare

Automated pipelines clean, deduplicate, and format your data into training-ready datasets with PII redaction applied.

03

Train

Fine-tuning runs on managed GPU infrastructure. You get progress updates and estimated completion times throughout.

04

Evaluate

The trained model is benchmarked against your domain-specific test set. Only models that pass quality gates are promoted to production.

01

Collect

Connect your data sources. We ingest documents, conversation logs, structured records, and any other domain data you have.

02

Prepare

Automated pipelines clean, deduplicate, and format your data into training-ready datasets with PII redaction applied.

03

Train

Fine-tuning runs on managed GPU infrastructure. You get progress updates and estimated completion times throughout.

04

Evaluate

The trained model is benchmarked against your domain-specific test set. Only models that pass quality gates are promoted to production.

FAQ

Common questions

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